Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
권호 표지
DOI QR코드

DOI QR Code

Analysis of COVID-19 Context-awareness based on Clustering Algorithm

클러스터링 알고리즘기반의 COVID-19 상황인식 분석

  • Lee, Kangwhan (Department of Computer Science Engineering, Korea University of Technology and Education)
  • Received : 2022.02.08
  • Accepted : 2022.03.23
  • Published : 2022.05.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper propose a clustered algorithm that possible more efficient COVID-19 disease learning prediction within clustering using context-aware attribute information. In typically, clustering of COVID-19 diseases provides to classify interrelationships within disease cluster information in the clustering process. The clustering data will be as a degrade factor if new or newly processing information during treated as contaminated factors in comparative interrelationships information. In this paper, we have shown the solving the problems and developed a clustering algorithm that can extracting disease correlation information in using K-means algorithm. According to their attributes from disease clusters using accumulated information and interrelationships clustering, the proposed algorithm analyzes the disease correlation clustering possible and centering points. The proposed algorithm showed improved adaptability to prediction accuracy of the classification management system in terms of learning as a group of multiple disease attribute information of COVID-19 through the applied simulation results.

본 논문에서는 학습 예측이 가능한 군집적 알고리즘으로 COVID-19에서 상황인식정보인 질병의 속성정보와 클러스터링를 이용한 군집적 알고리즘을 제안한다. 클러스터링 내에서 처리되는 군집 데이터는 신규 또는 새롭게 입력되는 정보가 상호관계를 예측하기 위해 분류 제공되는데, 이때 새롭게 입력되는 정보가 비교정보에서 오염된 정보로 처리되면 기존 분류된 군집으로부터 벗어나게 되어 군집성을 저하시키는 요인으로 작용하게 된다. 본 논문에서는 COVID-19에서의 질병속성 정보내 K-means알고리즘을 이용함에 있어 이러한 문제를 해결하기 위해 질병 상호관계 정보 추출이 가능한 사용자 군집 분석 방식을 제안하고자 한다. 제안하는 알고리즘은 자율적인 사용자 군집 특징의 상호관계를 분석학습하고 이를 통하여 사용자 질병속성간에 따른 클러스터를 구성해 사용자의 누적 정보로부터 클러스터의 중심점을 제공하게 된다. 논문에서 제안된 COVID-19의 다중질병 속성정보군집단위로 분류하고 학습하는 알고리즘은 적용한 모의실험 결과를 통해 사용자 관리 시스템의 예측정확도가 학습과정에서 향상됨을 보여주었다.

Keywords

Acknowledgement

This research was supported by the education research promotion program of Koreatech in 2020

References

  1. F. Rustam, A. A. Reshi, A. Mehmood, S. Ullah, B. -W. On, W. Aslam, and G. S. Choi, "COVID-19 Future Forecasting Using Supervised Machine Learning Models," IEEE Access, vol. 8, pp. 101489-101499, May. 2020. https://doi.org/10.1109/access.2020.2997311
  2. G. Adami, P. Avesani, and D. Sona, "Clustering documents in a web directory," in Proceedings of the 5th ACM international workshop on Web information and data management, New Orleans, USA, vol. 54, no. 3, pp.66-73, Sep. 2015.
  3. S. Makridakis, E. Spiliotis, and V. Assimakopoulos, "Statistical and machine learning forecasting methods: Concerns and ways forward," PLoS ONE, vol. 13, no. 3, pp. 1-26, Mar. 2018.
  4. E. Min, X. Guo, Q. Liu, G. Zhang, J. Cui, and J. Long. "A survey of clustering with deep learning: From the perspective of network architecture," IEEE Access, vol. 6, pp. 39501-39514, Jul. 2018. https://doi.org/10.1109/access.2018.2855437
  5. J. A. Hartigan and M. A. Wong, "Algorithm AS 136: A K-Means Clustering Algorithm," Journal of the Royal Statistical Society. Series C (Applied Statistics), vol. 28, no. 1, pp. 100-108, Jan. 2012.
  6. A. Likas, N. Vlassis, and J. J. Verbeek, "The global k-means clustering algorithm," Pattern Recognition, vol. 36, no. 2, pp. 451-461, Feb. 2003. https://doi.org/10.1016/S0031-3203(02)00060-2
  7. L. Xue and W. Luan, "Improved K-means Algorithm in User Behavior Analysis," in Ninth International Conference on Frontier of Computer Science and Technology, Dalian, China, pp. 339-342, 2015.
  8. H. Xiong, J. Wu, and J. Chen, "K-means Clustering versus Validation Measures: A Data Distribution Perspective," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 39, no. 2, pp. 318-331, Dec. 2008. https://doi.org/10.1109/TSMCB.2008.2004559
  9. Kangwhan-Lee, "Context-awareness User Analysis based on Clustering Algorithm," Journal of the Korea Institute of Information and Communication Engineering, vol. 24, no. 7, pp. 943-948, Jul. 2020
  10. T. Obichi, Y. Okaie, T. Nakano, T. Hara, and S. Nishio, "Inbody Mobile Bionanosensor Networks Through Non-diffusion-based Molecular Communication," in 2015 IEEE International Conference on Communications, London, U.K, pp. 1078-1084, 2015.
  11. D. L. Davies and D. W. Bouldin, "A Cluster Separation Measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 224-227, Apr. 1979.
  12. J. C. Dunn, "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters," J. Cybernetics, vol. 3, no. 3, pp. 32- 57, Apr. 2008. https://doi.org/10.1080/01969727308546046
  13. CDC [Internet]. Available: https://covid.cdc.gov/covid-data-tracker/support.html
  14. NYC Health [Internet]. Available: https://www1.nyc.gov/site/doh/covid/covid-19-data.page#daily